News in AI: when is ML worth it and for what, particle physics, healthcare

A market intelligence newsletter covering AI in the technology industry, research lab and venture capital market.

…10 days from 23rd November 2015. I’ll be publishing an analysis and commentary on financings and exits for H2 2015 soon, as well as sharing content from a talk I gave last night at Re.Work’s investing in deep learning dinner in London.

Technology news, trends and opinions

When is Machine Learning Worth it? A pragmatic piece by Ferenc Huszar who presents three scenarios in which ML techniques have differing value add: 1) some problems can be solved with simple heuristics, 2) others require ML to even come close to being solved (e.g. speech/object recognition), while a last category 3) see iterative ML improvements as a means to scale performance of an existing system (e.g. trading). Certainly the first camp is where you often hear companies pitch a cursory inclusion of ML in their product, while the second/third categories are where the meat of value creation lies.

Where are the Opportunities for Machine Learning Startups. Points to search, healthcare, cybersecurity and tools to improve the efficiency with which knowledge professional complete repetitive tasks. I’m with her on all of these points, but still on the lookout for the mammoth opportunity within healthcare. In the not too distant future, I’m betting we’ll be non-invasively monitoring physiology and lifestyle in real time (nature and nurture) to detect anomalies from a healthy state, produce more accurate diagnoses and and predict treatment response to improve healthcare outcomes.

Artificial Intelligence Called in to Tackle LHC Data Deluge. The two largest Large Hadron Collider experiments, which discovered the Higgs boson in 2012, produce hundreds of millions of collisions per second. Here, machine learning is used to help decide which collisions are of interest based on existing knowledge of prior events. A hot topic of discussion was whether deep learning could be applied to particle physics to discover new particles.

Machine Intelligence in the Real World. A discussion of market entry points for technologists working in the space. It’s a helpful framework when assessing new opportunities. Given the pace at which technology goes open source or is productised and provided for next to nothing, data and talent truly moves the needle. We see a renewed focus on building a solution to an unsolved/poorly served high-value, persistent problem for consumers or businesses vs. generalist services.

Research, development and resources

A Roadmap towards Machine Intelligence, Facebook AI Research and University of Trento. The authors present the general characteristics (namely communication and learning) that define intelligence machines and propose that we should be focusing on modelling all aspects of intelligence holistically within a single system. They present a simulated environment to aid in this regard.

Towards Principled Unsupervised Learning, Google Brain and Google DeepMind. Unlike supervised learning where the goal of a system is to minimise training error using labeled examples, unsupervised learning often deals with insufficient labelled examples. This makes it difficult for unsupervised cost functions to know which of the many possible supervised tasks the author cares about. Here, a new unsupervised cost function is presented and tested on speech recognition to prove efficacy on training functions without the use of input-output examples.

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks, Georgia Institute of Technology. The authors use electronic health records (diagnosis, procedures and medications) of 250k patients over 8 years to prove that RNNs can be used to predict future medical events and the timing of these events. Moreover, an RNN trained on data from one hospital can be used to improve predictions for another hospital with insufficient patient records.